System for Surgical Decisions Using Deep Learning

ABSTRACT

A method of monitoring and treating a patient using physiological data, a computer learning system, and a predictive model. The method may include generating predicted physiological data for a patient that is compared to a predictive model. When the predicted physiological data is comparable to the predictive model, preemptive care is administered.

CROSS REFERENCES TO RELATED PATENT

This application is a continuation of prior application Ser. No.16/199,030, filed on Nov. 23, 2018.

TECHNICAL FIELD

This invention relates to the improvement of the effectiveness of asurgeon's decision during an operation by predicting the proceduralsuccess rates and possible complications that could arise from theoperation. Furthermore, a deep learning model can predict in real timethe operating costs of certain procedures, prevention of medical fraud,life expectancy of patient, and, anesthesia time left until patientawakens. Finally, the next surgical steps can be predicted by a visionsystem using deep neural networks which can further enhance the medicalcare given during a complex medical operation.

BACKGROUND ART

Since the beginning of surgical history, doctors make their decisionsbased on their clinical and surgical experiences. Surgeons tend toprovide better patient care as they age because of past experiences andby developing a basic standard of risks across every single patientbased on statistical data. Although surgeons are well-respected fortheir level of education, surgeons don't always pick the perfectprocedure to implement given a patient's condition. They also don't comeacross certain experiences that another surgeon may have in his or hercareer. Surgeons are human beings and prone to make mistakes as anynormal human being would. Yet, computers do not make mistakes and canpotentially acquire knowledge equivalent too and eventually beyond asurgeon's capabilities by using artificial intelligence.

If there was supporting data showing the best possible way to proceed atany point during a surgery, a surgeon would clearly pick this best path.This data could change the course of a case if the surgeon knew therisks during surgery and it could change the outcome given that thesurgeon would make a certain decision or not based on the riskspresented. However, surgeons don't currently have this kind of data inthe operation room and it becomes more complicated as the surgerycommences because the patient's condition can quickly change at anymoment. Yet, if surgeons could have some technology with this knowledgethat can also take into account any sudden changes in the patient giventhe vitals of that patient or the particular step in the surgery, thenthis can greatly change the course of patient care and save more lives.

There has been a rise of robotic surgeries in the past decade withrobots making the surgical cut as opposed to human beings. The Da Vincimachine has been used in order to make more efficient and smaller cuts.In terms of cosmetic look, it's been a popular opinion to chose a moreminimally invasive approach such as the Da Vinci to prevent largerpermanent scars. It also prevents humanly mistakes such as an accidentalcuts or not precise cuts from occurring. While the surgeon is still thebrain behind the machine, the machine is making the physical cut.Another application just on the rise is using machine learning to helpthe surgeon make a surgical decision as opposed to just surgeons makingtheir own decisions. Similar to the use of the Da Vinci machine,surgeons are using technology but they are still making the decisions.There probably won't be technology completely taking over surgeon's jobsfor years to come. While machine learning takes data and can makepredictive algorithms, it cannot recognize its own issues within its ownalgorithm without human assistance. However, with the introduction ofdeep learning, technology is acting more and more like the human braincontinuing to learn and gather information becoming smarter and smarterover time. Deep learning is better than machine learning because it canrecognize potential issues within its own system as more information isinput and correct these issues without humans. It is actually a betterfit in terms of healthcare since healthcare is widely founded onstatistics and data. Every procedure done had to be successfullyconducted thousands of times in order to be trusted and continuously beused. Surgeons and doctors use this historical data before beginning anyprocedure. Likewise, deep learning works in the sense that it interpretsdata representation and mass data to build highly accurate models.

Surgeons often use an FDA approved risk calculator to decide whether ornot the surgery is worth the risk of someone's life. This calculatoralso includes other complications that could possibly arise during orafter surgery. Millions and millions of entries are entered everydayinto this calculator which ultimately builds its accuracy. However,surgeons usually do this before consulting a particular patient on theircondition and before actually operating on him or her. Ananesthesiologist or surgery could pull up this calculator duringsurgery. However, it is impractical to enter in all the patient's vitalstatistics into this calculator continuously during the surgery and it'smore of a generalization as opposed to recognizing what can occur eachstep within a case. Surgeons are therefore lacking live statistics thatcan note the potential risks or complications that could arise given aspecific time point in surgery. Moreover, the risk calculator is basedon historical patient history, but does not take in account currentpatient changing risks as the surgery is performed. It also doesn't takeinto account any changes in the patients state between the time isentered into the calculator and the present time. There is an assumptionthat the patient stays the same which may not necessarily be true. Inaddition, the risk calculator is more of a historical average versustaking into account the extremes within the population which happens atboth ends of the population. These extremes cause unpredicted risks in aprocedure that the current risk calculator cannot usually predict.

A major issue that medicine faces would be the extremes that exist onthe mass population curve, also known as the p-curve. Most people existin the middle of the p-curve and this is where surgeries don'tnecessarily have “random” complications. If someone dies from the middleof the p-curve, there is usually a clear reason as to why this happened.However, the people that exist on the ends of the curve are theinexplicable cases or the certain procedures that went awry and itwasn't until the death that there was a clearer explanation. Forexample, there has been situations where a patient dies because of anallergy to penicillin that the patient didn't know about prior tosurgery. With technological advancements, the p-curve will widen andless people will be at the ends of the curve. These “random”complications will soon not be denoted as random because of thispredicative model proposed. It might be surprising to see that theextreme cases might have similarities to each other in which they canpotentially be compared to each other. Eventually, the extreme can alsobe analyzed alongside someone in the median population who could havecharacteristics like someone labeled as an extreme. Therefore, there isa potential for the p-curve to widen and transfer those on the extremeend to the middle of the curve.

Some science fiction movies have introduced an interesting concept wherecurrency is built around life expectancy. For example, someone's life isprojected onto their wrists and either time can speed up or slow downdepending on how time was spent. In a similar manner, imagine ifsomeone's life can be counted down. Well human beings aren't omnipotent,but what if life expectancy can be projected when someone is in criticalcondition such as on an operation table. Impossible some might say toknow when someone is going to die. Surgeons have spent centuries tryingto estimate how much time someone has left to live based on statisticsand data. Yet, they have been focusing on this data prior to or aftersurgery as opposed to on the table. However, the current patentdescribed herein can predict someone's life expectancy to the lastsecond in real time while the patient is in surgery. Furthermore, thepatent described herein can provide real time risk percentages based onthe surgeon's selected procedure and the possible complications thatcould arise given a chosen path.

This presented system will work similarly to 1980's personal computervideo games such as the labyrinth 3d video game. Originally, these videogames were developed to be three dimensional but actually they wereprogrammed to be two dimensional with set three dimensional views. Thisinvention will work similarly since there are limited amount of variableand choices to be made in a procedure especially because most proceduresare done the same and are repeated. In a labyrinth, it started out wherethere was only two directions that the player could proceed in. Howeverover time, more choices could be made by the player as the game wasdeveloped such as maybe going diagonally, backwards, and grabbingobjects. In a similar matter, the surgical decision system can workwhere it starts at a simplified level and grows to include morevariables and add more layers to the matrix.

Each time a surgery begins, a labyrinth is basically drawn out whichrepresents the steps taken to get to the final end goal of having both ahealthy and alive patient. Each labyrinth is drawn specifically relativeto that patient, their vitals, what specific point they are in surgerygiven the video system and usage of object recognition, and the matchinglogic to other patient's similar to him or her. The labyrinth can changeat any point but the system learns what is the best path to take to theend. This logic can be applied to anesthesia time, life expectancy,anesthesia medication dosages which are all variables added on to thelabyrinth. This can also apply to the later suggested topic of usingscans to find the best pathway to location of surgery in procedures suchas the minimally invasive TAVR.

Today's operating room consist of data that is separated into differentservers and devices. This makes comparing data very difficult. Theoperating room is connected to the cloud, but the security concerns ofmoving patient data is prevalent throughout our society. Moreover, thereis not a direct and dynamic displaying of the patient's expense perprocedure during an operation. This patent attempts to solve theseissues so that a physician has a heads up display of the surgical risks,operating expenses, and schedule of events such as time left for patientto awake.

To date, there is no prior art that allows a real time predictive systemthat optimizes surgical decisions based on historic patient data, realtime operating data, cost, prior surgical decisions, and provides anautonomous surgical prediction based on real time and historical datalike previous operating procedures captured with video. The presentinvention learns the operating room procedures using video, sound, andsurgical instrument's geotags without direct input from the operatingroom staff. This allows a real-time ongoing predictive model.Furthermore, the invention described herein builds a plurality ofpredictive models instead of just one for a faster and more accurateresult. The predictive model is chosen at a certain step in the surgeryand used for the most accurate prediction. Some attempted solutions havetried building encoded neural networks using Deep Learning, but none ofthem have a methodology to pick a different model during a medicalprocedure based on the current medical decision.

DISCLOSURE OF INVENTION

These and other problems are generally solved or circumvented, andtechnical advantages are generally achieved, by preferred embodiments ofthe present invention that provides for an advanced predictive methodfor surgery, specifically to a mechanism for predicting surgicaldecisions, procedural success rates, surgical complications,pharmaceutical decisions, and cost analysis using deep learningtechniques.

Research has shown that the success of an operation depends on thepatient's personal characteristics and the ability of the physician. Therisks of a complex operation is based on the patient's heredity factors,health risks, ongoing health concerns, and unknown health issues. Thispatent described herein explains a method of how to lower risks andprovide the best path forward during an operation. It will also shortenthe length of surgeries especially when picking the next operationalprocedures as the surgery progresses. Instead of taking time making afirm decision, this program will help the surgeon make the bestdecisions based on a deep learning model.

The presently preferred embodiment of the present invention utilizes aneural network on a processing device that has been trained on thehistorical patient's physical data including hereditary factors, pastmedical history, and ongoing health concerns utilizing deep learningmethods. This processing device will also be trained with surgicaldecisions using previous surgical videos with sound and deep learningalgorithms similar to autonomous driving training with graphicalprocessing units such that the processing device can generate aplurality of predictive models.

The second part of the presently preferred embodiment of the presentinvention is a method to choose a model from the previously generatedplurality of predictive pre-generated models in correspondence to thecurrent surgical decision. The method consist of a surgeon providingsurgical decisions during the operation. The surgical decisions will beused to search for corresponding single predictive model from theplurality of predictive pre-generated models that references the currentsurgical decision.

The third part of the presently preferred embodiment of the presentinvention is a method to securely evaluate the current patient's vitalstatistics and physician's procedural decision versus the chosen trainedmodel. This evaluation using the chosen trained model which isdynamically calculated provides a predictive cost, life expectancy,patient risk, anesthesiology timeline, and surgical procedure timelinewhich can be viewed securely by the physician or operating staff.

The fourth part of the presently preferred embodiment of the presentinvention is a detailed patient progress report which includes specificinformation regarding the patient's surgery including ongoing cost,procedural risks, predictive allergies or health concerns, lifeexpectancy, anesthesiology timeline, and next suggested procedure. Thefifth part of this presently preferred embodiment is the prediction ofthe next procedure that the surgeon will take based on ongoingoperational awareness using either video, sound, mouse, keyboard orgeotags on surgical tools or a combination of all these inputs. As soonas one particular path is chosen by the surgeon, the life expectancyclock will be updated and a suggested next procedure or pharmaceuticalmedicine to be given will be chosen by the deep learning algorithm.

Accordingly, besides the objects and advantages of an surgical decisionmaking using deep learning described herein, and additional objectiveand advantage of the preferred embodiment of the present invention is toprovide a secure method of interchanging data from different servers andmedical devices using a secure blockchain communication method.

Another additional objective and advantage of the preferred embodimentof the present invention is to provide a secure method of interchangingdata from different servers and medical devices using a secure point topoint communication method, encrypted communication method, orblockchain method.

Another objective and advantage to this invention is to decrease actualsurgery time because a procedural guideline with associated risks can bedisplayed to the surgeon as opposed to searching for a small detailabout the patient that would not be immediately known.

Another objective and advantage to this invention is to predict theamount of time in each section of the surgery and the cost associatedwith each section. It also could predict the time it would take tocomplete other suggested procedures with corresponding cost to complete.This would allow the surgeon to provide the lowest risk care with theassociated cost. Today surgeon's chose the best procedure for theirpatient, but in the case where two or more procedures were essentiallyequivalent, this would provide the surgeon with the lowest costprocedure with the fastest completion time.

Another objective and advantage to this patent is to assist surgeonssimilar to autonomous driving. Today's cars assist drivers so that thereis limited intervention. This patent will allow surgeons to focus on thesurgery while getting live predictive data that dynamically changes withtheir procedural steps in the operation. In the future, just like withautonomous driving, surgeons will use this patent with roboticassistance to completely have autonomous surgery. This will allow thesurgeon to intervene only when absolutely necessary allowing theprocessing device to complete the surgery independent of all humanassistance. Essentially, this patent is a stepping stone to autonomoussurgery.

Another objective and advantage to this patent is to identify possiblerisks that are not known about the patient by the surgeon. These riskscould be allergies, hereditary traits, hereditary risks, or othercomplications that came up with a patient's family members during theirsurgeries. Most of the time surgeries proceed as normal, but sometimesrisks occur that need expedited resolution, however, vital informationmay not be known by the surgeon since the patient is unconscious.

Another objective and advantage to this patent is to identify thepotential for a patient to awaken from under a general anesthesia. Ithas been shown that patients who are asleep are sometimes awake duringan operation. This can be a fearful event for both the patient andanesthesiologist. However, a predictive model using patient data andoperating data such as brain waves could help identify when the patientis not in a deep sleep.

Another objective and advantage to this patent is to identify when apatient is not within the normal population of previous patients. Thiswill help the surgeon understand that there is additional risks with thecurrent operation. This patent will help match data to drive apredictive model of relative characteristics of a patient that coulddrive major complications and timelines such as surgical procedures,life expectancy, time to awake, anesthesia medication dosages throughoutthe surgery, and operating cost.

Another objective and advantage to this patent is to use a combinationof computer vision and geotags that will recognize and take into accounteach surgical tool or medication that is used in the surgery. This willlessen the amount of time to prepare for surgery and inventory will nolonger need to be done since it will already be taken into account for.This inventory will also take part in the running cost of the surgery.This can also apply to medications and taking into account whatmedications are present. The geotags and computer vision can also insurethat only an authorized user can use the instruments.

Another objective and advantage to patent is that wireless connectivitysuch as bluetooth can portably move anesthesia time on a small wirelessdisplay as the patient leaves the operation room. This will allow boththe nurses and anesthesiologists to have a general knowledge as to whenthe patient will wake so they can plan accordingly.

Another objective and advantage to the patent is that the system willalso be able to predict dosage sizes for certain medications at certaintimes throughout the surgery given by the matching historical data ofother patient's. It will also be correlated to the other variables thathas been discussed such as vital signs and the surgery vision system forfurther support. This will give the anesthesiologist more information inorder to support their general knowledge surrounding dosage sizes. Also,the dosages will take part in the running costs. The system will knowhow much dosage is used based on flow control which will be takenaccount in cost and the amount of dosages that was originally predicted.

Another objective and advantage to the patent is that the system willnot only have video cameras interpreting the surgery but also videocameras over where the anesthesiologists are. This will work in asimilar manner to the other camera in which it will recognize certainmedications based on packaging or label names and take this informationinto account in the system. It is also acts as a prevention of medicalfraud where an anesthesiologist cannot accurately record whatmedications were used and when they were used during surgery. This willtake part in the running cost calculations.

Another objective and advantage to patent is that the system iscustomizable for each surgeon. The surgeon is recognized either thruvoice or video recognition. The system is that calibrated towards thesurgeons operating room settings. Also, the system could recognize thepatient in a similar fashion and have the operating room be setup fortheir specific characteristics such as temperature of the room. Surgeonsalso usually like to play music while operating so the system can uploadmusic that the particular patient likes to work to. The system will alsobe able to learn not only what type of music the surgeon likes, but alsothe environment the surgeon likes to work best in. Also, the system willsoon be able to be more customizable for different specialties. Forexample, the anesthesiologist can have a version or tab with informationrelevant to them such as predicted and anesthesia time or predicteddosages. Cardiology will be able to have adaptations such as for TAVRwhere a routing system might be needed. This can also apply to neurologywhich is generally a very complex and tedious specialty. Therefore, it'simportant to be very careful in brain surgery since one slight movementin the wrong place can make major damage.

An additional embodiment of this present invention is to provide aprogram will include certain possible procedures to choose from givenpast data and success rates and failure rates. The program will rank theprocedures based on relevancy and the best possible routes to proceedin. As soon as one particular path is picked, a life expectancy clockwill be projected. There will be a separate tab to input the particularpatient's statistics prior to surgery such as gender, age, healthconditions, past surgeries, present health concerns. This will be usefulalso to quickly reference and act as the patient's chart. It will alsobe important so that past patient data can be matched up with thecurrent patient based on similar characteristics. This will allow thereto be a list the potential complications given past mass data. Vocalrecognition can also be applied to this tab if the surgeon needs to knowa basic statistic about the patient. Again, it will also waste less timeas opposed to searching for a small detail about the patient thatwouldn't be known immediately. The invention will also use its owninterpretation of scans and a predicted life expectancy associated witha given a certain procedure. These scans will actually be read morethoroughly and accurate as opposed to a human brain would despiteexperience. In the future, a camera can be linked up to the program toreceive live data images of both medical scans, such as CAT scans or anechocardiogram, and live video footage which will increase the accuracyof the both the life expectancy, procedures to give, a possibility ofthe patient coming out of anesthesia, and major complications that couldarise. Vocal recognition will be used for easy access such as asking aquestion of how to proceed in a given situation or specific knowledgethat needs to be known about the patient anytime throughout the case. Assoon as a certain step in the procedure is chosen, the program will makeits adjustments and take this into account especially in terms of thepredicted life expectancy clock and other variables like anesthesiatime, dosage amount, risk percentages, major complications that couldarise, and surgical decisions to make given the situation.

Another objective and advantage of the additional embodiment of thepatent is a projected path for surgeries that are guided with scansusing the surgeries live video feed or cat scans. This can apply tocertain specialties like cardiology or in neurology. In cardiology, theTAVR procedure, valve replacements are done through the femoral artery,are becoming more common but there isn't necessarily a clear path on thescan of how this is done. Therefore, this system will provide guidancewith a highlighted projected path with also vocal recognition that willprovide directions to the intended destination. This will be done basedon the matching logic to similar patients and use similar logic tolabyrinth metaphor addressed earlier. Also, the program will use objectrecognition in order to recognize a particular route it should proceedgiven that the program will be fed many scans into the system ofhealthy, blocked, or narrow arteries. This will allow for thecombination of surgery rooms video system and live feed inside thepatient for greater accuracy of the machine learning system.

Another objective and advantage of the additional embodiment of thepatent is to provide complications that could arise for going the wrongpath inside a patient. These complications could be shown on the displayand warn the surgeon of mistakes during surgery. The system couldprovide new directions to a final destination similar to navigationsystems in a car. The system could also provide rerouting options when aobstacle becomes apparent. This works similar to the traffic logic inthe navigation system where the best route is given. However, it will beup to the doctor to proceed in a certain direction and the system willadjust accordingly. This also is a good teaching tool for youngerdoctors without as much experience as well. However, it can be used atany experience level of a health professional. The system will take thisroute into account in terms of life expectancy and any complicationsthat could arise especially if the wrong route is taken.

Another objective and advantage of the additional embodiment of thispatent is that the video interpretation system will record what is seenin the surgery in text form which will eventually eliminate the need fordoctor's notes. Doctors can add additional information at the end ofsurgery but it won't be necessary since this system saw what everyone inthe room did. This reduce the amount of workload for healthprofessionals. Given that surgeons already spend so many hours in theoperating room, this will also decrease their long work hours throughoutthe week. This will give the surgeons more rest time and possibly moreopportunity to focus in the operating room as opposed to spending timewriting these notes. Also, medical scribes in the operation room will nolonger be needed. A health care technical word bank can be input intothe system and the system can be taught how to write like aprofessional. This documentation will also be more accurate than a humanbeing trying to recall what happened based off of memory. Furthermore,this information will take part in matching other patients to eachother. The system will then become more efficient with time andexperience. It will soon be able to interpret its own data input asopposed to human information put into the system. This will also preventdoctors from inaccurately reporting what happened in surgery thuspreventing medical fraud. Furthermore, there will be less room forgrammatical error that can lead to misinterpretation of data.

Another objective and advantage of the additional embodiment of thispatent is to use DNA or hereditary searches for the patient so that amore exact model can be used. The system could use patient data or DNAdata to pull up records and data to assist the accuracy of predictions.

Another objective and advantage of the additional embodiment of thispatent is to use a combination of surgical data, patient data, andpatient hereditary data to predict blood clots and to provide surgicaldecisions to overcome this obstacle.

Still further objects and advantages will become apparent from aconsideration of the ensuing description and drawings. Similarconstructions that do not depart from the spirit and scope of thisinvention set forth in the claims or embodiment should be considered theequivalent.

BRIEF DESCRIPTION OF DRAWINGS

For a more complete understanding of the present invention, and theadvantages thereof, reference is now made to the following descriptionstaken in conjunction with the accompanying drawing, in which:

FIG. 1 is a diagrammatic illustration of a system for surgical decisionsusing deep learning; and

FIG. 2 is a diagrammatic illustration of a semi autonomous surgicalsystem with limited surgeon intervention that is used in combinationwith a deep learning algorithm; and

FIG. 3 is a diagrammatic illustration of the lowest risk surgical pathwhich depends on a predictive deep learning model.

MODE(S) FOR CARRYING OUT THE INVENTION Detailed Description of Best Modefor Carrying Out the Invention

The making and using of the presently preferred embodiments arediscussed in detail below. It should be appreciated, however, that thepresent invention provides many applicable inventive concepts that canbe embodied in a wide variety of specific contexts. The specificembodiments discussed are merely illustrative of specific ways to makeand use the invention, and do not limit the scope of the invention.

The present invention will be described with respect to preferredembodiments in a specific context, namely an advanced predictive methodof surgery, specifically to a predictive mechanism for procedural risks,operating costs of certain procedures, life expectancy, and anesthesiatime left until patient awakes.

Operation of Best Mode for Carrying Out the Invention

Referring now to the drawings in detail, and initially to FIG. 1, asystem for surgical decisions using deep learning. A patient (101) ishaving an operation on a specific medical issue. The patient (101) isscanned (102) for vital statistics (103) for the ongoing operation.Historical data (104) consisting of previous surgical vital statistics,hereditary data, prescription drug history, and ongoing medical issuesmay be found in local computer or cloud records and submitted (106) tothe deep learning processor (111). The deep learning processor (111)will generate a plurality of predictive models (112) based on thesubmitted historical data (104). A surgeon (107) provides (108) surgicaldecisions (107) during the operation submitted (110) to a deep learningprocessor (111). The surgical decisions (107) will be used to search forcorresponding single predictive model that references the currentsurgical decision. The next predicted surgical decisions can bepreloaded in the deep learning processors to increase speed ofcalculation. The vital statistics (103) is used as input (105) to a deeplearning processor (111) that is encoded with the single predictivemodel (112). The predictive model (112) provides an output correspondingto the lowest risk surgical decision, time expectancy, time for patientto wake, waiting room updates, and surgical risks displayed on an outputlike a display, augmented reality glasses, or mobile device.

Referring now to vital statistics (103) in detail in FIG. 1. Vitalstatistics could be sensors reading information like oxygen level, brainpatterns, electrocardiogram, blood content, heart scans, body scans,organ scans, muscle scans, and bone scans.

Referring now to deep learning processor (111) in detail in FIG. 1. Adeep learning processor utilizes a neural network to recognize patternsthat works well in a systematic setting. The operating room is filledwith systematic procedures in which a certain procedure is repeated overand over again. In the operating room, there are displays that projectthe operations on the screens to assist everyone in the room with havinga better view of the operation. It becomes more difficult to see whenmore and more people surround the patient. Therefore, there is videocameras that basically show what the surgeon is seeing. There will alsobe other video camera interpreting and recognizing the medications thatthe anesthesiologists are using. From this footage, the program couldlive footage as to how the operation is preceding. It'll be able tointerpret this in a similar manner as to how autonomous cars read signson the street. This is known as computer vision basically through theuse of neural networks to interpret data. Billions of different versionsof the same image are input into a program and then the computer is ableto interpret what it is. Videos are basically a sequence of images buttogether so this program will be able to interpret these images in thechronological order it is put into the system. In this manner, theprogram will be able to further predict the next step in the surgery. Asthe program becomes more and more intelligent, it'll be able tointerpret this footage and apply it to its statistics and calculations.As of now, the program will use past data and data input before thesurgery to assist the surgeon. However, in time the program can evolveto taking both past and live data into account. In a similar way to achild learning, the program will soon learn to become as knowledgeableor even more knowledgeable as a surgeon. As more surgeries are input,the smarter the program will become and apply this knowledge to pasthistorical data from patient files. If the videos taken in surgery aresomehow saved and specified under each patient, it'll make the program'spredictions more accurate. Eventually this program will be able topredict possible complications from live data before it happens withindications or warnings of something possibly going wrong before eventhe surgeon notices. This can also be applied in terms of anesthesiawhere there can be warnings as to the patient possibly coming out ofanesthesia.

Referring now to historical data (104) in detail from FIG. 1. Historicaldata can be related to other patient's surgery information that isavailable for processing. This invention could also use technology inorder to read input scans which contains files of the patients as wellas the scans. Epic software is an example of a form of data storage forall the patient information in a hospital, local regional area, ornationally connected medical centers. This software is passwordprotected for only use of a healthcare professional as well as onlybeing used in a hospital setting. Epic software's setup varies with eachspecialty as well as at different hospitals. Away to possibly gaininformation to be used in the deep learning model is through removal ofthe patient's names because that would follow HIPPAA protocol. Theprogram will only be used in a hospital setting and not taken out ofthat setting for any other use. Therefore, professionals will be usingthis information and they already have an agreement to keep personalinformation confidential for each patient. Eventually, the goal is tohave all the statistics in every operating room in the United States andmaybe even one day around the world. As stated elsewhere, the moreinformation the program has, the more accurate the diagnosis will be.Even though human beings are all different, they are very much the samein many ways.

Referring now to surgical decisions (107) in detail from FIG. 1.Surgical decisions may be entered through a mouse, keyboard, videointerpretation or audio commands. Surgeons often dictate the certainsteps they are going to do while operating to direct everyone in sort ofa team fashion. It is sort of a follow the leader dynamic. In which, theleader is the surgeon and he or she is dictating how to go about theparticular surgery. Their hands are preoccupied with doing the surgery.So, the best way to use the program would be through vocal recognitionsimilar to that used with the common voice recognition software onmobile phones or standalone voice devices. This program will act asanother tool to help the surgeon without interrupting his flow of work.It will also be used as a form of multitasking with the priority stillbeing the patient.

Referring now to deep learning processor (111) and plurality ofpredictive models (112). The making and using of a deep learningprocessor or deep learning model is essential to this patent. However,one skilled in the art of deep learning can develop this model usingadvanced GPU processors. It should be appreciated, however, that thepresent invention provides many applicable deep learning concepts thatcan be embodied in a wide variety of specific contexts. The specificembodiments discussed are merely illustrative of specific ways to makeand use the invention, and do not limit the scope of the invention.

Referring now to an output (113) in detail in FIG. 1. After a certainprocedure is chosen, a life expectancy clock will be provided to theoperating room. The patient's predicted life expectancy will bedisplayed based on the branching logic and ultimately the deep learninganalysis. The life expectancy clock can change as the deep learningalgorithm takes into account new data. The output (113) could bedisplayed on a graphical interface or augmented reality glasses thatonly certain hospital employees can view during an operation. Also, theoutput (113) could be a mobile application device running on a mobiledevice. Another output of this device could be waiting time andimportant health information to family members waiting in the waitingroom. Family members could see this information and be informed ofpredicted time to completion of the surgery. Moreover, the mobileapplication could also show the predicted and actual cost of the ongoingsurgery.

Another embodiment of the output (113) could display the predicted timelife for a patient to awake which would be provided to theanesthesiologist. According to Current Procedural Terminology (CPT)guidelines, anesthesia begins when the anesthesiologist prepares thepatient and then end as soon as the anesthesiologist is said to leavethe room, which is also when the surgery is over. Intubation is placingbreathing tubes through vocal cords to help a patient breath especiallyduring a surgery. Intubation time is measured from when a tube is placeddown a patient's trachea to assist with ventilation to when it isremoved. However, extubation refers to taking the breathing tubesoutside of a patient. Anesthesiologists make sure a patient can breatheon their own and usually determine this by seeing if a person is awakeor following commands. This extubation time would vary depending on thecomplexity of a case. For example, it would occur in the intensive careunit for a cardiovascular case as opposed to in the operation roomcausing a lengthier extubation time. Intubation time as well asextubation time are documented in patient records. Specifically,extubation time is noted for ICU cases such as those for cardiovascularsurgery. Extubation is usually done when a patient is awake andfollowing commands. This would indicate that a person is able to breatheon his or her own. Length of a case usually can correlate to how long aperson is awake or following commands. However, this is not always thecase. It usually refers to when a patent is in the operation room.Intubation time, extubation time, and length of a case are documented inpatients' records. All of which are variables that help predict whengeneral anesthesia wears off and the patient wakes up. There is ageneral time frame as to when a patient should wake up but there is yeta way to determine an exact time until he or she wakes up.

Anesthesiologists are trained to know the dosage sizes based on themedical literature and they formed their reasoning on dosages based inexperiments in which mass data of the effect of the particular dosageswere recorded. Similar to the risk calculators, anesthesiologists areusing statistics to make their decisions. There is also a pump whichgives a recommended dosage of each drug to the patient. Theanesthesiologist acts and is the final decider on whether the predictionis accurate. As opposed to the life expectancy clock, which will recorda patient's predicted life expectancy in minutes. An anesthesia clockcould predict a time in which a patient will awake based on variousvariables and a deep learning model. Some of theses variables caninclude length of a case, intubation time, extubation time, medicationdosages, family medical history, historical records of other patients,and an individual's statistics all of which are documented. This clockwill give the anesthesiologist extra reassurance that their dosagecalculations are correct. However, the clock may not be able to accountfor everything like genetics could play a part. This is a lackingknowledge that anesthesiologists are trying to figure out the influencesof dosages on some certain type of genetics. Since past data will berecorded in minutes and protocol for billing is all based on minutes,the clock will project out a time. Anesthesiologists use either onlinecalculators or hand calculations of dosages to give the particularpatient prior to surgery. The anesthesia time is recorded in patientrecords as a main form of billing, but it can also be used to take for amore efficient time accuracy. Alongside the calculations made by theanesthesiologist, this program will have past data of anesthesia timesof other patients. As described earlier, the program will match theparticular patient to another patient and from that, display the time.Ultimately, the anesthesia time will be more accurate as opposed to justhaving the calculation from the anesthesiologist. This will provide moreefficiency and a less probability of a patient coming out of anesthesiain the middle of a surgery. This will also lessen the risk of thepatient dying as well as the probability for a lawsuit especially sinceanesthesiologists are said to be sued frequently. However, this is justa prediction and nothing is for sure. It just gives a more accurate timeduration given past data.

Description of Additional Mode for Carrying Out Invention

The making and using of the additional preferred embodiments arediscussed in detail below. It should be appreciated, however, that thepresent invention provides many applicable inventive concepts that canbe embodied in a wide variety of specific contexts. The specificembodiments discussed are merely illustrative of specific ways to makeand use the invention, and do not limit the scope of the invention.

The present invention will be described with respect to an additionalembodiments in a specific context, namely surgical decisions using deeplearning to predict life expectancy, surgical decisions, and risks asshown in FIG. 1 using branch prediction or probability tree. Aprobability tree could be used in conjunction with the deep learningalgorithm so as to filter out unrelated patient or historical data. Thesystem will use a probability tree to search through the input patientrecords and match the current patient to its best match. Similar to thelogic of looking for an organ door or blood donor, the program will lookfor the closest match. Therefore, the more patient records input intothe program, the more accurate the match will be. There are billions ofpeople in the world and although everyone is different, they are verymuch the same in many ways. It is also similar to the logic of a datingwebsite. A dating website usually works in which a person inserts theirbasic information such as their hobbies, interests, or occupation. Inthe same way, patient's stats will be entered. The program will usebranching logic to match the patient to a match. It'll first start at acharacteristic such as gender, then branch off to age, branch off topast medical history including hereditary and DNA, then branch off tosmaller details until it finds the closest match based on past records.Eventually, it will reach a branching point where it'll try to match acertain procedure to the original procedure input into the program.Eventually, the program can evolve to take into account a quick changeof procedure to apply to the deep learning portion of the invention.After the branching logic commences, the program will get to a pointwhere procedures will be chosen. Based on the pick, a clock will startrunning after it—takes into a count the branching logic. This is where amajority of deep learning commences. Deep learning is a form of datarepresentation in time analysis of deep learning. From this logic, thelife expectancy clock and the anesthesia clock will run.

Operation of an Alternate Mode for Carrying Out the Invention

Referring now to the drawings in detail, and specifically to FIG. 2,which is a semi-autonomous surgical system with limited surgeonintervention that is used in combination with a deep learning algorithm.A patient (201) is having an operation on a specific medical issue. Thepatient (201) is viewed (202) by an operation video camera (203) andscanned (202) for vital statistics (203) for the ongoing operation.Historical data (204) consisting of previous surgical vital statistics,hereditary data, prescription drug history, and ongoing medical issuesmay be found in computer or cloud records and submitted (206) to thedeep learning processor(s) (211). The deep learning processor(s) (211)will generate a plurality of predictive models (212) based on thehistorical data (204). Moreover, a surgeon (207) completing theoperation inputs (208) his surgical decisions using an input device(209). The input device (209) could be keyboard, mouse, voice, or actualvideo of the operation. The input device (209) also is feed into theneural network (211). The surgical decisions (208) will be used tosearch for corresponding single predictive model that references thecurrent surgical decision. The next surgical decisions can be preloadedin the deep learning processors to increase speed of calculation. Thevideo and vital statistics (203) is used as input (206) to a deeplearning processor(s) (211) that is encoded with the single predictivemodel (213). The predictive model (213) provides an output correspondingto the lowest risk surgical decision, time expectancy, time for patientto wake, waiting room updates, and surgical risks displayed on an outputlike a display or mobile device.

Referring now to neural network (211) and predictive model (212). Themaking and using of a neural network or deep learning model is essentialto this patent. However, one skilled in the art of neural network ordeep learning can develop this model with the assistance of advanced GPUprocessors. It should be appreciated, however, that the presentinvention provides many applicable deep learning methods that can beembodied in a wide variety of specific contexts. The specificembodiments discussed are merely illustrative of specific ways to makeand use the invention, and do not limit the scope of the invention.

Referring now to FIG. 3, which relates to the methodology of decidingwhich encoded model is loaded into the neural network, the initialprocedure 1 encoded model (301) is loaded into the neural network. Apatient begins the operation and the patient's vital statistics andoperation inputs are passed into the procedure 1 encoded model (301).Procedure 2 encoded model (304) is predicted (302) by procedure 1encoded model (301). Also, procedure 3 encoded model (305) is predicted(303) by procedure 1 encoded model (301). Both procedure 2 encoded model(304) and procedure 3 encoded model (305) can be preloaded into a neuralnetwork for the next surgical decision. Based on the doctor's directionthough video or input device, procedure 2 encoded model (304) orprocedure 3 encoded model (305) will be chosen. If procedure 3 encodedmodel (305) is chosen, it is the last procedure in the patient'soperation. Based on the number of procedures in an operation, aprediction on length of time can be generated. If procedure 2 encodedmodel (304) is chosen, the patient's vital statistics and operationinputs are then passed into the new model. Procedure 4 encoded model(307) is predicted (306) by procedure 2 encoded model (304). It is theonly procedure model predicted. The patient's vital statistics andoperation inputs are then passed into the new chosen procedure 4 encodedmodel (307). Procedure 5 encoded model (308) is predicted (310) byprocedure 4 encoded model (301). Also, procedure 6 encoded model (311)is predicted (309) by procedure 4 encoded model (301). Both procedure 5encoded model (308) and procedure 6 encoded model (311) can be preloadedinto a neural network for the next surgical decision. Based on thedoctor's direction though video or input device, procedure 5 encodedmodel (308) or procedure 6 encoded model (311) will be chosen. Ifprocedure 5 encoded model (308) or procedure 6 encode model (311) arechosen, it is the last procedure in the patient's operation.

INDUSTRIAL APPLICABILITY

Accordingly, the industrial applicability of this patent is for asellable biomedical device allowing for reliable surgical decision usingdeep learning, specifically to an autonomous surgical prediction usingvideo and audio deep learning of the next surgical decision.

SEQUENCE LISTING

Not applicable

I claim:
 1. A method of monitoring and treating a patient usingphysiological data, a computer learning system, and a predictive model,the method comprising: inputting into the computer learning systemreference physiological data for previous patients that experienced asurgical complication; generating, in the computer learning system, apredictive model corresponding to the reference physiological data forpatients that experienced the surgical complication; inputting into thecomputer learning system a patient's current physiological data that iscontemporaneously obtained from a physiological monitor during asurgery; generating predicted future physiological data for the patientbased on the patient's current physiological data and the predictivemodel; comparing the patient's future predicted physiological data tothe predictive model; and if the patient's predicted futurephysiological data is comparable to the predictive model, thenadministering preemptive care for the surgical complication.
 2. Themethod of claim 1, wherein the computer learning system comprises a deeplearning processor.
 3. The method of claim 1, wherein administeringpreemptive care for the surgical complication is selected from the groupconsisting of omitting surgical actions, administering medication,conducting diagnostic testing, and performing surgical actions.
 4. Themethod of claim 1, further comprising predicting a date and time whenthe surgical complication will occur based on the comparison of thepatient's predicted physiological data with the predictive model.
 5. Themethod of claim 4, further comprising, if the surgical complication ispredicted to occur during a surgery, then administering preemptive careduring the surgery.
 6. The method of claim 5, wherein administeringpreemptive care for the surgical complication during surgery is selectedfrom the group consisting of administering medication, avoiding surgicalactions, conducting diagnostic testing, and performing surgical actions.7. The method of claim 4, further comprising providing the surgicalcomplication and the predicted date and time on a display for viewing bya medical provider.
 8. The method of claim 7, wherein the predicted dateand time are provided on the display in the form of a countdown clock.9. The method of claim 1, wherein the patient's physiological data isselected from the group consisting of pulse, oxygen level, brain wavepatterns, electrocardiogram data, blood test results, and organ scans.10. The method of claim 5, further comprising providing to a medicalsurgical participant, on a display, identification of the preemptivecare to be administered during surgery.
 11. The method of claim 5,further comprising generating and providing to a medical surgicalparticipant, on a display and based on at least the surgicalcomplication, identification of contraindicated actions that should notbe taken during surgery.
 12. The method of claim 1, whereinadministering preemptive care comprises the implementation ofnon-surgical actions and the avoidance of further surgical actions. 13.The method of claim 1, wherein the surgical complication is organfailure.
 14. The method of claim 1, wherein the surgical complication isstroke.